As an emerging computing paradigm, edge computing offers computing resources closer to the data sources, helping to improve the service quality of many real-time applications. A crucial problem is designing a rational pricing mechanism to maximize the revenue of the edge computing service provider (ECSP). However, prior works have considerable limitations: clients are static and are required to disclose their preferences, which is impractical in reality. However, previous works assume user privacy information to be known or consider the number of users in edge scenarios to be static. To address this issue, we propose a novel sequential computation offloading mechanism, where the ECSP posts prices of computing resources with different configurations to clients in turn. Clients independently choose which computing resources to purchase and how to offload based on their prices. Then Egret, a deep reinforcement learning-based approach that achieves maximum revenue, is proposed. Egret determines the optimal price and visiting orders online without considering clients' preferences. Experimental results show that the revenue of ECSP in Egret is only 1.29\% lower than Oracle and 23.43\% better than the state-of-the-art when the client arrives dynamically.

翻译：作为一种新兴计算范式，边缘计算通过将计算资源部署至更接近数据源的位置，有助于提升众多实时应用的服务质量。其中关键问题在于设计合理的定价机制以最大化边缘计算服务提供商（ECSP）的收益。然而，现有研究存在显著局限性：客户端需保持静态且须披露自身偏好，这在现实中难以实现。此外，既有工作或假定用户隐私信息已知，或认为边缘场景中用户数量恒定不变。针对上述问题，本文提出一种新颖的序列化计算卸载机制：ECSP依次向客户端发布不同配置计算资源的价格，客户端则根据价格独立决策购买何种计算资源及卸载方式。进而提出Egret——一种基于深度强化学习实现收益最大化的方法。该方法无需考虑客户端偏好即可在线确定最优定价与访问顺序。实验结果表明，在客户端动态到达场景下，采用Egret机制的ECSP收益仅比Oracle方案低1.29%，较最新技术提升23.43%。